[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122145-en":3,"doc-seo-122145-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122145,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Platelet phenotyping using spectral flow cytometry and machine learning - PhD thesis","Research focuses on platelet phenotyping using spectral flow cytometry combined with machine learning to enable multiparameter characterization of platelet states. The thesis develops and applies analytical workflows for high-dimensional cytometry data, including bioinformatics support for downstream analysis and specialized flow cytometry core capabilities for sorting platelet samples. It evaluates how machine learning approaches can capture relevant phenotype features and quantify effects of agonists, supporting improved understanding of platelet biology during ageing and functional modulation.","Platelet phenotyping using spectral flow cytometry and machine learning  \nA thesis submitted in partial fulfilment of the degree of  \nDoctor of Philosophy  \n2020-2024  \nAmi Vadgama  \nSupervisors: Professor Tim Warner and Dr. Paul Armstrong  \nThe Blizard Institute  \nFaculty of Medicine and Dentistry  \nQueen Mary University of London  \nLondon, United Kingdom  \nStatement of Originality  \nI, Ami Vadgama, hereby confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by, others that this is duly acknowledged below, and my contribution indicated. Previously published material is also acknowledged below.  \nI attest that I have exercised reasonable care to ensure that the work is original, and does not , to the best of my knowledge , break any UK law, infringe any third party’s copyright or other Intellectual Property Right, or contain any confidential material. I accept that the College has the right to use plagiarism detection software to check the electronic version of the thesis. I confirm that this thesis has not been previously submitted for the award of a degree by this or any other university.  \nThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author.  \nSignature: Ami Vadgama  \nDate: Wednesday 13th March 2024  \nAcknowledgements  \nI would like to say a huge thank you to Tim for letting me complete my PhD in the Warner Lab. Tim, thank you for all of your support, supervision, time, care, encouragement, (dark) humour, and patience – you have always gone above and beyond for me, both professionally and personally, and I can’t thank you enough. To Paul, thank you too for your support, supervision, time, care, encouragement,(terrible) humour, and patience, but most importantly, for coping with my everpresent (and usually rampant) state of anxiety. Paul, I am honoured to be your first, and Tim, I am honoured to be your last.  \nHarriet and Nicola, thank you for your reassurance, continual willingness to help me, big hugs, pep talks, coffee and walks, and teaching me that it is ok to make mistakes. Harriet, you have always been my unofficial third supervisor, and I feel so lucky to have had you. Mari, thank you for teaching me how to work with platelets and how to (unsuccessfully) speak Italian. James, thank you for all of your bioinformatics expertise, consistent patience in explaining things to me, and the grace with which you answer my (many, many) questions. Ella, thank you for your support and help, for being as excited about flow cytometry as me, and knitting guidance. Trauma Paul and Suthesh, I have loved learning about the clinical side of research. To all of the Warner Lab, from the lab to the office to all the conferences and Big Days/Nights Out, I have had the absolute best 4 years with you all.  \nThank you to my wonderful family, especially my grandparents, for always being so enthusiastic and excited about what I’m doing. To my parents, the biggest thank you for constantly encouraging me and feeding my curiosity growing up; and to my brother, for being the absolute opposite to me in every which way possible. A special thank you to my endlessly patient partner, Oli, and to my dog, Chester – who I sincerely hope will take the time to read this – for his (largely) unconditional love.  \nTo all of the brilliant friends I have made at the Blizard (Helen, Claire, Eve, Izzy, Isabell, Rachel, Myfi, Michael, Lewis, George, Vijay, Jack, and everyone else) and  \nduring my PhD (Laura and Nicki), I am so glad to have met you, and for all the fun we have had and will have. A particular thanks to Helen for being the sanity to my insanity.  \nTo all of my other friends (Amaarah, Seema, Annika, and Timmy), thank you for never leaving my side and keeping me afloat – I would, unquestionably, not be where I am without you, and I am forever grate","cbCais6LZhnEzbuo","https://ap.wps.com/l/cbCais6LZhnEzbuo","pdf",12095242,1,256,"English","en",105,"# Statement of Originality\n# Acknowledgements\n# Details of Collaborations\n# Details of Publications\n## Papers","[{\"question\":\"What is the central topic of this thesis?\",\"answer\":\"The thesis centers on platelet phenotyping using spectral flow cytometry integrated with machine learning for multiparameter analysis.\"},{\"question\":\"How does the work connect data generation and analysis?\",\"answer\":\"It combines flow cytometry core facilities for platelet sorting with bioinformatics analysis support for downstream processing (referenced around later chapters).\"},{\"question\":\"What kind of outcomes are investigated using machine learning?\",\"answer\":\"The research characterizes platelet phenotypes and assesses the effects of agonists by leveraging machine learning on multiparameter measurements.\"}]","Platelet phenotyping using spectral flow cytometry and machine learning - 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